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        "author": "Leo Goodstadt",
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            "Intended Audience :: Information Technology",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Bio-Informatics",
            "Topic :: Software Development :: Build Tools",
            "Topic :: Software Development :: Libraries",
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        "description": "\n***************************************\nOverview\n***************************************\n\n\n    The Ruffus module is a lightweight way to add support\n    for running computational pipelines.\n\n    Computational pipelines are often conceptually quite simple, especially\n    if we breakdown the process into simple stages, or separate **tasks**.\n\n    Each stage or **task** in a computational pipeline is represented by a python function\n    Each python function can be called in parallel to run multiple **jobs**.\n\n    Ruffus was originally designed for use in bioinformatics to analyse multiple genome\n    data sets.\n\n***************************************\nDocumentation\n***************************************\n\n    Ruffus documentation can be found `here <http://www.ruffus.org.uk>`__ ,\n    with `download notes <http://www.ruffus.org.uk/installation.html>`__ ,\n    a `tutorial <http://www.ruffus.org.uk/tutorials/new_tutorial/introduction.html>`__ and\n    an `in-depth manual <http://www.ruffus.org.uk/tutorials/new_tutorial/manual_contents.html>`__ .\n\n\n***************************************\nBackground\n***************************************\n\n    The purpose of a pipeline is to determine automatically which parts of a multi-stage\n    process needs to be run and in what order in order to reach an objective (\"targets\")\n\n    Computational pipelines, especially for analysing large scientific datasets are\n    in widespread use.\n    However, even a conceptually simple series of steps can be difficult to set up and\n    maintain.\n\n***************************************\nDesign\n***************************************\n    The ruffus module has the following design goals:\n\n        * Lightweight\n        * Scalable / Flexible / Powerful\n        * Standard Python\n        * Unintrusive\n        * As simple as possible\n\n***************************************\nFeatures\n***************************************\n\n    Automatic support for\n\n        * Managing dependencies\n        * Parallel jobs, including dispatching work to computational clusters\n        * Re-starting from arbitrary points, especially after errors (checkpointing)\n        * Display of the pipeline as a flowchart\n        * Managing complex pipeline topologies\n\n\n***************************************\nA Simple example\n***************************************\n\n        Use the **@follows(...)** python decorator before the function definitions::\n\n            from ruffus import *\n            import sys\n\n            def first_task():\n                print \"First task\"\n\n            @follows(first_task)\n            def second_task():\n                print \"Second task\"\n\n            @follows(second_task)\n            def final_task():\n                print \"Final task\"\n\n\n\n\n        the ``@follows`` decorator indicate that the ``first_task`` function precedes ``second_task`` in\n        the pipeline.\n\n        The canonical Ruffus decorator is ``@transform`` which **transforms** data flowing down a\n        computational pipeline from one stage to teh next.\n\n********\nUsage\n********\n\n    Each stage or **task** in a computational pipeline is represented by a python function\n    Each python function can be called in parallel to run multiple **jobs**.\n\n    1. Import module::\n\n            import ruffus\n\n\n    1. Annotate functions with python decorators\n\n    2. Print dependency graph if you necessary\n\n        - For a graphical flowchart in ``jpg``, ``svg``, ``dot``, ``png``, ``ps``, ``gif`` formats::\n\n            pipeline_printout_graph (\"flowchart.svg\")\n\n        This requires ``dot`` to be installed\n\n        - For a text printout of all jobs ::\n\n            pipeline_printout(sys.stdout)\n\n\n    3. Run the pipeline::\n\n        pipeline_run()\n\n\n",
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